Applications of three-dimensional (3D) optical image sensing and visualization technologies span a wide range of areas including TV broadcasting, 3D displays, entertainment, medical sciences, and robotics. An advantage of 3D in relation to traditional two-dimensional (2D) imaging techniques is its capability to capture the structural information of different objects that are in a scene. One promising 3D approach is based on integral imaging or integral photography, which is an autostereoscopic imaging method used to capture 3D information and to visualize it in 3D space, either optically or computationally. The system operates under incoherent or ambient light, thus sensing can be performed passively unlike holography or laser detection and ranging (LADAR) that require lasers. Integral imaging can provide the 3D profile and range of the objects in the scene, thus it is attractive for 3D object recognition. The number of cameras and pixels and the sensing parallax that are optimum for image pickup depend on a number of parameters including object size, distance, and depth.
Recognizing human activity from visual sources has been receiving significant attention from the computer vision and machine learning communities over the last few decades.
Regarding the visual source, most of the past work on action recognition has focused on monocular gray-level 2D sequences. Recently, some efforts have been made to use depth information, mainly because of the popularization of RGB-D systems. There are three main approaches for depth sensing based on stereo, time of flight, and structured light, and these have been applied to human sensing in general and for gesture recognition in particular. Integral imaging can contribute to this area because of its capability to reconstruct 3D scenes at different depths. This information, as well as the other benefits of integral imaging discussed above, can be used to help in the action recognition process.
These and other needs are effectively addressed by the systems and methods of the present disclosure.